Although microarray technology has been widely applied to the
analysis of many malignancies, integrative analyses across multiple
studies are rarely investigated, especially for studies of
different platforms or studies of different diseases. Difficulties
with the technology include issues such as different experimental
designs between studies, gene matching, inter-study normalization
and disease heterogeneity. This dissertation is motivated by these
issues and investigates two aspects of inter-study analysis. First,
we aimed to enhance the inter-study prediction of microarray data
from different platforms. Normalization is a critical step for
direct inter-study prediction because it applies a prediction model
established in one study to data in another study. We found that
gene-specific discrepancies in the expression intensity levels
across studies often exist even after proper sample-wise
normalization, which cause major difficulties in direct inter-study
prediction. We proposed a sample-wise normalization followed by a
ratio-adjusted gene-wise normalization (SN+rGN) method to solve
this issue. Taking into account both binary classification and
survival risk predictions, simulation results, as well as
applications to three lung cancer data sets and two prostate cancer
data sets, showed a significant and robust improvement in our
method. Second, we performed an integrative analysis on the
expression profiles of four published studies to detect the common
biomarkers among them. The identified predictive biomarkers
achieved high predictive accuracy similar to using whole genome
sequence in the within-cancer-type prediction. They also performed
superior to the method using whole genome sequences in
inter-cancer-type prediction. The results suggest that the compact
lists of predictive biomarkers are important in cancer development
and represent common signatures of malignancies of multiple cancer
types. Pathway analysis revealed important tumorogenesis functional
categories. Our research improved predictions across clinical
centers and across diseases and is a necessary step for clinical
translation research in public health.